Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning
R. Montalà, B. Font, P. Suárez, J. Rabault, O. Lehmkuhl, R. Vinuesa, I. Rodriguez
Published: 2025/9/12
Abstract
In this work, deep reinforcement learning (DRL) is applied to active flow control (AFC) over a threedimensional SD7003 wing at a Reynolds number of Re = 60,000 and angle of attack of AoA = 14 degrees. In the uncontrolled baseline case, the flow exhibits massive separation and a fully turbulent wake. Using a GPU-accelerated CFD solver and multi-agent training, DRL discovers control strategies that enhance lift (79%), reduce drag (65%), and improve aerodynamic efficiency (408%). Flow visualizations confirm reattachment of the separated shear layer, demonstrating the potential of DRL for complex and turbulent flows.